W. J. ZENG

I work on research, development and strategy at
rigetti quantum
computing. I am focused on quantum computer architecture, quantum software
and algorithm engineering, and making quantum computing useful.
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I am product lead for the
forest
experimental quantum programming toolkit, including the open source libraries
pyquil
and
grove.
My
phd thesis
at oxford applies abstract methods (mainly from categorical algebra) to the
study of quantum algorithms and protocols. Before that I worked with superconducting
qubit systems at
yale
and
eth zurich.
If you want to learn more about how to get started with quantum computing, I've written a
short introduction, which is part of the
documentation for
pyQuil.

The first scalable universal quantum computers are now available, such as the 19
qubit processor built by Rigetti Computing. As these devices mature, it is
important to consider how best to make use of them. This requires new and applied
programming models for quantum computing. In particular, promising near-term
algorithms for quantum simulation, optimization, and machine learning require a
hybrid quantum/classical programming environment. In this talk, we introduce a
freely available open-source environment (Forest)
based on a shared-memory intermediate representation
( Quil) that is optimized for
this hybrid model. The environment runs through a cloud API with client-side Python
libraries that target both superconducting quantum circuit and classical simulation
backends. We will share how to get started with Forest, as well as how to do
research with example hybrid algorithms using a quantum computer.

We are excited to share that our team has demonstrated unsupervised machine learning
using 19Q, our new 19-qubit general purpose superconducting quantum processor. We did
this with a quantum/classical hybrid algorithm for clustering developed at Rigetti...

Machine learning techniques have led to broad adoption of a statistical model of
computing. The statistical distributions natively available on quantum processors
are a superset of those available classically. Harnessing this attribute has the
potential to accelerate or otherwise improve machine learning relative to purely
classical performance. A key challenge toward that goal is learning to hybridize
classical computing resources and traditional learning techniques with the emerging
capabilities of general purpose quantum processors. Here, we demonstrate such
hybridization by training a 19-qubit gate model processor to solve a clustering
problem, a foundational challenge in unsupervised learning. We use the quantum
approximate optimization algorithm in conjunction with a gradient-free Bayesian
optimization to train the quantum machine. This quantum/classical hybrid algorithm
shows robustness to realistic noise, and we find evidence that classical optimization
can be used to train around both coherent and incoherent imperfections.

We outline three developments that are needed over the next five years to ensure
that the first quantum computers can be programmed to perform useful tasks. First,
developers must think in terms of 'hybrid' approaches that combine classical and
quantum processors. For example, at Rigetti we have developed an interface called
Quil, which includes a set of basic instructions for managing quantum gates and
classical processors and for reading and writing to and from shared memory. Second,
researchers and engineers must build and use open-source software for
quantum computing applications. Third, scientists need to establish a
quantum programming community to nurture an ecosystem of software. This community
must be interdisciplinary, inclusive and focused on applications.

We show that parametric coupling techniques can be used to generate selective
entangling interactions for multi-qubit processors. By inducing coherent population
exchange between adjacent qubits under frequency modulation, we implement a
universal gateset for a linear array of four superconducting qubits. An average
process fidelity of F=93% is estimated for three two-qubit gates via quantum
process tomography. We establish the suitability of these techniques for
computation by preparing a four-qubit maximally entangled state and comparing the
estimated state fidelity against the expected performance of the individual
entangling gates. In addition, we prepare an eight-qubit register in all possible
bitstring permutations and monitor the fidelity of a two-qubit gate across one pair
of these qubits. Across all such permutations, an average fidelity of F=91.6±2.6%
is observed. These results thus offer a path to a scalable architecture with high
selectivity and low crosstalk.

We propose and implement a family of entangling qubit operations activated by
radio-frequency flux pulses. By parametrically modulating the frequency of a
tunable transmon, these operations selectively actuate resonant exchange of
excitations with a statically coupled, but otherwise off-resonant, neighboring
transmon. This direct exchange of excitations between qubits obviates the need for
mediator qubits or resonator modes, and it allows for the full utilization of all
qubits in a scalable architecture. Moreover, we are able to activate three
highly-selective resonances, corresponding to two different classes of entangling
gates that enable universal quantum computation: an iSWAP and a controlled-Z
rotation. This selectivity is enabled by resonance conditions that depend both on
frequency and amplitude, and is helpful in avoiding frequency crowding in a
scalable architecture. We report average process fidelities of F = 0.93 for a
135 ns iSWAP, and F = 0.92 for 175 ns and 270 ns controlled-Z operations.

Quantum Computing Institute Seminar, Oak Ridge National Lab, TN
The first scalable universal quantum computing devices are now being designed and
built in several groups worldwide. As these devices mature, it is important to
consider how best to make use of them. This will require new and applied
programming models for quantum computing. In particular, promising near-term
algorithms for quantum simulation, quantum chemistry, and optimization require a
hybrid quantum/classical programming environment. In this talk, we introduce an
open-source environment (Forest) based on a
shared-memory intermediate
representation (Quil).
The environment runs through a cloud API with client-side
Python libraries that can target both superconducting quantum circuit and classical
simulation backends. We discuss the programming model and implementations of
the
Quantum Approximate Optimization Algorithm in this environment.

Recent progress on quantum computing hardware, especially in superconducting qubit
systems, highlights the need for practical programming models and tools for these
first devices. In particular, many near-term applications are quantum/classical
hybrid algorithms, that treat the quantum computer as a co-processor. In this
workshop, we introduce
Forest,
an open source quantum programming toolkit targeting near-term
applications and devices. This toolkit includes an intermediate quantum instruction
language (Quil) and Python libraries for generating and executing Quil code in
either a simulated environment or on a quantum processor.

Quantum computing devices based on superconducting quantum circuits have rapidly
developed in the last few years. The building blocks-superconducting qubits, quantum-limited
amplifiers, and two-qubit gates-have been demonstrated by several groups. Small prototype
quantum processor systems have been implemented with performance adequate to demonstrate
quantum chemistry simulations, optimization algorithms, and enable experimental tests of
quantum error correction schemes. A major bottleneck in the effort to develop larger systems
is the need for a scalable functional architecture that combines all the core building blocks
in a single, scalable technology. We describe such a functional architecture, based on a planar
lattice of transmon and fluxonium qubits, parametric amplifiers, and a novel fast DC
controlled two-qubit gate.

We introduce an abstract machine architecture for classical/quantum
computations---including compilation---along with a quantum instruction
language called Quil for explicitly writing these computations. With this formalism,
we discuss concrete implementations of the machine and non-trivial algorithms
targeting them. The introduction of this machine dovetails with ongoing development of
quantum computing technology, and makes possible portable descriptions of recent
classical/quantum algorithms.

We propose a new application of quantum computing to the field of natural
language processing. Ongoing work in this field attempts to incorporate
grammatical structure into algorithms that compute meaning. In (Coecke,
Sadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model)
based on tensor product composition. While this algorithm has many advantages,
its implementation is hampered by the large classical computational resources
that it requires. In this work we show how computational shortcomings of the CSC
approach could be resolved using quantum computation (possibly in addition to
existing techniques for dimension reduction). We address the value of quantum
RAM (Giovannetti,2008) for this model and extend an algorithm from Wiebe, Braun
and Lloyd (2012) into a quantum algorithm to categorize sentences in CSC. Our new
algorithm demonstrates a quadratic speedup over classical methods under certain conditions.

Baltimore, MD -
APS March Meeting 2016
Gauge color codes are topological quantum error correcting codes on three dimensional
lattices. They have garnered recent interest due to two important properties: (1) they
admit a universal transversal gate set, and (2) their structure allows reliable error
correction using syndrome data obtained from a measurement circuit of constant depth.
Both of these properties make gauge color codes intriguing candidates for low overhead
fault-tolerant quantum computation. Recent work by Brown et al. calculated a threshold
of ~0.31\% for a particular gauge color code lattice using a simple clustering decoder
and phenomenological noise. We show that we can achieve improved threshold error rates
using the efficient Wootton and Loss Markov-chain Monte Carlo (MCMC) decoding. In the
case of the surface code, the MCMC decoder produced a threshold close to that code's
upper bound. While no upper bound is known for gauge color codes, the thresholds we
present here may give a better estimate.

Quantum information brings together theories of physics and computer science. This
synthesis challenges the basic intuitions of both fields. In this thesis, we show that
adopting a unified and general language for process theories advances foundations and
practical applications of quantum information. Our first set of results analyze quantum
algorithms with a process theoretic structure. We contribute new constructions of the Fourier
transform and Pontryagin duality in dagger symmetric monoidal categories. We then use this
setting to study generalized unitary oracles and give a new quantum blackbox algorithm for
the identification of group homomorphisms, solving the GROUPHOMID problem. In the remaining
section, we construct a novel model of quantum blackbox algorithms in non-deterministic
classical computation. Our second set of results concerns quantum foundations. We complete
work begun by Coecke et al., definitively connecting the Mermin non-locality of a process
theory with a simple algebraic condition on that theory's phase groups. This result allows
us to offer new experimental tests for Mermin non-locality and new protocols for quantum secret
sharing. In our final chapter, we exploit the shared process theoretic structure of quantum
information and distributional compositional linguistics. We propose a quantum algorithm adapted
from Weibe et al. to classify sentences by meaning. The clarity of the process theoretic setting
allows us to recover a speedup that is lost in the naive application of the algorithm. The main
mathematical tools used in this thesis are group theory (esp. Fourier theory on finite groups),
monoidal category theory, and categorical algebra.

.................................... Contextuality and the Weak Axiom in the Theory of Choice
[Proc. Quantum Inter.]

23 November, 2015

Recent work on the logical structure of non-locality has constructed scenarios
where observations of multi-partite systems cannot be adequately described by
compositions of non-signaling subsystems. In this paper we apply these frameworks
to economics. First we construct a empirical model of choice, where choices are
understood as observable outcomes in a certain sense. An analysis of contextuality
within this framework allows us to characterize which scenarios allow for the possible
construction of an adequate global choice rule. In essence, we mathematically
characterize when it makes sense to consider the choices of a group as composed of
individual choices. We then map out the logical space of some relevant empirical
principles, relating properties of these contextual choice scenarios to no-signalling
theories and to the weak axiom of revealed preference.

We show that a pair of complementary dagger-Frobenius algebras, equipped with a
self-conjugate comonoid homomorphism onto one of the algebras, produce a nontrivial
unitary morphism on the product of the algebras. This gives an abstract
understanding of the structure of an oracle in a quantum computation, and we apply
this understanding to develop a new algorithm for the deterministic identification
of group homomorphisms into abelian groups. We also discuss an application to the
categorical theory of signal-flow networks.